The present invention relates generally to discovering, classifying and detecting alarm patterns and more particularly, to a method of discovering, classifying and detecting alarm patterns for electrophysiological monitoring systems.
Monitoring personnel, such as a physician or nurse, may use an electrophysiological monitoring system to simultaneously monitor multiple health parameters such as blood pressure, heart rhythm, heart rate, and specific oxygen to determine a health condition of a patient. Typically, electrophysiological monitoring systems raise alarms when a monitored signal value crosses a threshold. Alarms may also be raised when a specific waveform or waveform property is detected in a short segment of a recorded signal, e.g., a moving window average. For example, if a patient's heart rate exceeds a certain level or threshold, an alarm may be recognized and generated.
With such traditional detection methods, too many alarms may be generated to be of medical significance. That is, one alarm for a particular patient condition may be insignificant on its own. However, when the alarm is found in a sequence or group of alarms, it may indicate a particular patient health condition. Additionally, when insignificant alarms are generated, medical staff time is frivolously utilized in investigating such alarms, and when too many insignificant alarms are generated, medical staff may begin to ignore or to place a low priority in such alarms. When this occurs, a valid alarm may be ignored or treated with less urgency during a critical period, thus endangering the patient. Furthermore, additional non-critical alarms may be recognized due to faulty sensors, equipment malfunctions, or patient movement. These “non-actionable alarms” divert resources of medical personnel to non-critical alarms and reduce the efficiency of the monitoring process.
Therefore it would be beneficial to discover and detect alarm patterns in an alarm sequence to identify critical health conditions in order to reduce the number of non-critical alarms. However, known alarm pattern detection methods are based on time series signal processing methods that may fail to discover and/or recognize certain alarm patterns that are extended over a long period of time. In addition, alarm patterns may not be properly detected if they are interrupted by another non-critical alarm also referred to as an ‘interdigitated alarm.’ Furthermore, known alarm pattern detection methods may only detect a critical medical condition after the entire alarm sequence is completed.
Therefore, it would be beneficial to design an alarm pattern discovery, detection, and classification method that reduces the number of non-critical alarms, discovers alarm patterns from multiple concurrent and sequential alarm signals over an extended period of time, and can classify an alarm sequence with a medical condition before the alarm sequence is complete.